Mechatronics fault detection system

Abstract

The invention provides a mechatronics fault detection system, which belongs to the mechatronics fault detection field and comprises a sensor module, a self-adaptive sensor configuration module, a data acquisition module, a data fusion processing module, a fault diagnosis module, a virtual reality and augmented reality interaction module and a user interface module. And through multi-sensor data fusion, the monitoring accuracy is improved. And the advanced fault diagnosis algorithm is applied, so that the fault identification time is remarkably shortened. Compared with the prior art, potential faults are recognized in advance, the shutdown risk of equipment is reduced, the maintenance cost is saved, an advanced mathematical model and a complex formula are comprehensively utilized, and high efficiency and reliability of mechatronics fault detection are achieved through systematic design and an innovative algorithm. Through the integration of the modules, the operation efficiency and reliability of the equipment can be greatly improved, and the maintenance cost and the downtime are reduced.

Description

Technical Field

[0001] The invention belongs to the field of mechatronic fault detection, and in particular relates to a mechatronic fault detection system. Background Art

[0002] With the advancement of science and technology, various mechatronic detection systems emerge in an endless stream. However, the existing mechatronic fault detection systems face many problems in practical applications, mainly including the following aspects:

[0003] 1. Low data quality

[0004] Noise interference: Sensors are easily affected by electromagnetic interference, vibration and other factors in the working environment, resulting in large data noise.

[0005] Missing data: Network instability or sensor failure may cause the loss of important data and affect the overall performance of the system.

[0006] 2. Insufficient feature extraction

[0007] Improper feature selection: Existing systems often rely on traditional feature extraction methods and fail to fully explore the potential features in complex signals.

[0008] High-dimensional data processing: When the data volume is large and the dimension is high, existing algorithms will encounter difficulties in processing and analyzing, resulting in poor fault identification results.

[0009] 3. Insufficient accuracy of fault diagnosis

[0010] Algorithm limitations: Some existing algorithms (such as traditional machine learning algorithms) may not be able to effectively capture complex nonlinear failure modes, resulting in false positives and false negatives.

[0011] Poor model generalization ability: Under different working conditions, failure modes often change, and the existing models are not adaptable enough, resulting in reduced diagnostic accuracy when new situations arise.

[0012] 4. Difficulty in updating pattern libraries

[0013] Lack of dynamic update mechanism: The fault mode library in existing systems is often static and cannot be updated in time according to new fault types, resulting in reduced diagnostic efficiency.

[0014] The historical data accumulated over a long period of time has not been fully utilized: the historical failure data has not been systematically processed, making it difficult to build a more diversified failure mode library.

[0015] 5. Poor real-time performance

[0016] Data processing delays: Complex data processing algorithms may cause delays, making real-time monitoring and rapid response impossible.

[0017] Computing resource limitations: In some application scenarios, large amounts of data cannot be processed quickly due to limited computing resources.

[0018] 6. Poor User Experience

[0019] Complex interface: The user interface design is not user-friendly and the operation is complicated, which makes it difficult for users to quickly understand and use the system.

[0020] Lack of customization: Failure to provide flexible customized alarm and monitoring settings to meet user needs.

[0021] 7. Cost and maintenance issues

[0022] High equipment cost: The high procurement cost of high-performance sensors and data processing equipment affects the popularity of the system.

[0023] Difficulty in subsequent maintenance: The system's troubleshooting and maintenance rely on professional technicians, which increases maintenance difficulty and cost.

[0024] In summary, the current mechatronic fault detection system has certain problems in data quality, feature extraction, fault diagnosis, real-time performance, user experience, etc. In order to solve these problems, it is necessary to continuously improve sensor technology, optimize algorithms, enhance user interface design, and build a dynamically updated fault mode library to enhance the overall performance and reliability of the system.

[0025] Therefore, there is an urgent need in the art for a mechatronic detection technology solution that can solve one or more of the above problems.

[0026] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0027] The purpose of the present invention is to provide a technical solution for real-time monitoring and diagnosis of the health status of electromechanical equipment through multi-sensor data fusion and complex signal processing algorithms.

[0028] To achieve the above object, the present invention provides the following solutions:

[0029] A mechatronic fault detection system, comprising:

[0030] Sensor module, adaptive sensor configuration module, data acquisition module, data fusion processing module, fault diagnosis module, virtual reality and augmented reality interaction module and user interface module.

[0031] Optionally, the sensor module includes: a vibration sensor, a temperature sensor, a pressure sensor and a current sensor;

[0032] The adaptive sensor configuration module is used to automatically select and adjust the use of sensors under different working conditions according to the equipment operating status and historical data.

[0033] Optionally, the data acquisition module is provided with an analog-to-digital converter for converting analog signals collected by the sensor into digital signals.

[0034] Optionally, the data fusion processing module fuses and cleans the multi-sensor data through Kalman filtering and wavelet transform.

[0035] Optionally, the fault diagnosis unit uses a mathematical model to predict faults based on a machine learning algorithm.

[0036] Optionally, the user interface provides visual monitoring and fault alarms.

[0037] Optionally, the formula of the Kalman filter is:

[0038] x k =Ax k-1 +Bu k +w k

[0039] z k =Hx k +v k

[0040] Among them, x k is the state estimation, A is the state transfer matrix, u k is the control input, H is the observation matrix, w k is the process noise, v k is the observation noise.

[0041] Optionally, the wavelet transform formula is:

[0042]

[0043] Among them, y(t) is the feature extracted by wavelet transform, c jk is the wavelet coefficient, ψ jk (t) is the wavelet basis function.

[0044] Optionally, the fault diagnosis model is specifically to use a support vector machine to classify faults, and its decision function is:

[0045]

[0046] Among them, K(x i,x) is the kernel function, b is the bias term, α i is the Lagrange multiplier;

[0047] The fault predictions shown are:

[0048] Combine historical data and use the autoregressive integrated moving average model to predict faults:

[0049] X t =φ1X t-1 +φ2X t-2 +…+φ p X t-p +θ1∈ t-1 +…+∈ q ∈ t-q +∈ t

[0050] Among them, X t is the time series value, φ is the autoregressive parameter, θ is the moving average parameter, and ε is the white noise.

[0051] Optionally, the user interface provides real-time monitoring data and fault warnings, and establishes fault alarm rules through threshold judgment and logistic regression models:

[0052] y=β0+β1X1+β2X2+...+β n X n ;

[0053] Where y is the predicted result value, usually 0 or 1, indicating a certain state. In fault detection, if y = 1, it means a fault has occurred; if y = 0, it means there is no fault;

[0054] β0 is the intercept term, which represents the benchmark of the prediction result when the values ​​of all features X are 0. It is the initial value of the model when there is no influence of any input features.

[0055] X1,X2,...,X n The input features represent the parameters or indicators monitored by the system. These features are used to predict the possibility of failure.

[0056] β1,β2,...,β n are regression coefficients, corresponding to the input features X1, X2, ..., X n , these coefficients measure the influence of each feature on the prediction result; these coefficients measure the influence of each feature on the prediction result;

[0057] The user interface uses virtual display and augmented reality modules to show operators the machine health status, fault location and recommended maintenance measures, enhancing the user experience; interactive alarm settings: users can select the sensitivity and type of alarms through touch screen or gestures.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The present invention provides a mechatronic fault detection system, which improves the accuracy of monitoring through multi-sensor data fusion. The use of advanced fault diagnosis algorithms significantly reduces the time for fault identification. Potential faults can be identified in advance, the risk of equipment downtime can be reduced, and maintenance costs can be saved. The present invention comprehensively utilizes advanced mathematical models and complex formulas, and through systematic design and innovative algorithms, the efficiency and reliability of mechatronic fault detection are achieved. Through the integration of the above modules, the operating efficiency and reliability of the equipment can be greatly improved, and maintenance costs and downtime can be reduced. The detection system not only has breakthroughs in technology, but also can provide significant improvements in user experience and data security, and has high market value and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0061] Figure 1 A schematic diagram of the structure of a mechatronic fault detection system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] The purpose of the present invention is to provide a technical solution for real-time monitoring and diagnosis of the health status of electromechanical equipment through multi-sensor data fusion and complex signal processing algorithms.

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0065] Embodiment 1:

[0066] The present invention provides a mechatronic fault detection system, such as Figure 1 As shown, including:

[0067] Sensor module, adaptive sensor configuration module, data acquisition module, data fusion processing module, fault diagnosis module, virtual reality and augmented reality interaction module and user interface module.

[0068] Further, the sensor module includes: a vibration sensor, a temperature sensor, a pressure sensor and a current sensor;

[0069] The adaptive sensor configuration module is used to automatically select and adjust the use of sensors under different working conditions according to the equipment operating status and historical data.

[0070] Furthermore, the data acquisition module is provided with an analog-to-digital converter for converting the analog signal collected by the sensor into a digital signal.

[0071] Furthermore, the data fusion processing module fuses and cleans the multi-sensor data through Kalman filtering and wavelet transform.

[0072] Furthermore, the fault diagnosis unit uses a mathematical model to predict faults based on a machine learning algorithm.

[0073] Furthermore, the user interface provides visual monitoring and fault alarms.

[0074] Furthermore, the formula of the Kalman filter is:

[0075] x k =Ax k-1 +Bu k +w k

[0076] z k =Hx k +v k

[0077] Among them, x k is the state estimation, A is the state transfer matrix, u k is the control input, H is the observation matrix, w k is the process noise, v k is the observation noise.

[0078] Furthermore, the formula of the wavelet transform is:

[0079]

[0080] Among them, y(t) is the feature extracted by wavelet transform, c jk is the wavelet coefficient, ψ jk (t) is the wavelet basis function.

[0081] Furthermore, the fault diagnosis model is specifically to use support vector machine to classify faults, and its decision function is:

[0082]

[0083] Among them, K(x i ,x) is the kernel function, b is the bias term, α i is the Lagrange multiplier;

[0084] The fault predictions shown are:

[0085] Combine historical data and use the autoregressive integrated moving average model to predict faults:

[0086] X t =φX t-1 +φ2X t-2 +...+φ p X t-p +θ1∈ t-1 +...+θ q ∈ t-q +∈ t

[0087] Among them, X t is the time series value, φ is the autoregressive parameter, θ is the moving average parameter, and ε is the white noise.

[0088] Furthermore, the user interface provides real-time monitoring data and fault warnings, and establishes fault alarm rules through threshold judgment and logistic regression models:

[0089] y=β0+β1X1+β2X2+...β n X n ;

[0090] Where y is the predicted result value, usually 0 or 1, indicating a certain state. In fault detection, if y = 1, it means a fault has occurred; if y = 0, it means there is no fault;

[0091] β0 is the intercept term, which represents the benchmark of the prediction result when the values ​​of all features X are 0. It is the initial value of the model when there is no influence of any input features.

[0092] X1,X2,...,X n The input features represent the parameters or indicators monitored by the system. These features are used to predict the possibility of failure.

[0093] β1,β2,...,β n are regression coefficients, corresponding to the input features X1, X2, ..., X n, these coefficients measure the influence of each feature on the prediction result; these coefficients measure the influence of each feature on the prediction result;

[0094] The user interface uses virtual display and augmented reality modules to show operators the machine health status, fault location and recommended maintenance measures, enhancing the user experience; interactive alarm settings: users can select the sensitivity and type of alarms through touch screen or gestures.

[0095] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0096] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A mechatronic fault detection system, characterized in that: include: Sensor module, adaptive sensor configuration module, data acquisition module, data fusion processing module, fault diagnosis module, virtual reality and augmented reality interaction module and user interface module.

2. The mechatronic fault detection system according to claim 1, characterized in that: The sensor module includes: a vibration sensor, a temperature sensor, a pressure sensor and a current sensor; The adaptive sensor configuration module is used to automatically select and adjust the use of sensors under different working conditions according to the equipment operating status and historical data.

3. The mechatronic fault detection system according to claim 1, characterized in that: The data acquisition module is provided with an analog-to-digital converter for converting the analog signal collected by the sensor into a digital signal.

4. The mechatronic fault detection system according to claim 1, characterized in that: The data fusion processing module fuses and cleans the multi-sensor data through Kalman filtering and wavelet transform.

5. The mechatronic fault detection system according to claim 1, characterized in that: The fault diagnosis unit uses a mathematical model to predict faults based on a machine learning algorithm.

6. The mechatronic fault detection system according to claim 1, characterized in that: The user interface provides visual monitoring and fault alarms.

7. The mechatronic fault detection system according to claim 1, characterized in that: The formula of the Kalman filter is: x k =Ax k-1 +Bu k +w k z k =Hx k +v k Among them, x k is the state estimation, A is the state transfer matrix, u k is the control input, H is the observation matrix, w k Process noise Sound, v k is the observation noise.

8. The mechatronic fault detection system according to claim 1, characterized in that: The formula of the wavelet transform is: Among them, y(t) is the feature extracted by wavelet transform, c jk is the wavelet coefficient, ψ jk (t) is the wavelet basis function.

9. The mechatronic fault detection system according to claim 5, characterized in that: The fault diagnosis model is specifically to use support vector machine to classify faults, and its decision function is: Among them, K(x i ,x) is the kernel function, b is the bias term, α i is the Lagrange multiplier; The fault predictions shown are: Combine historical data and use the autoregressive integrated moving average model to predict faults: X t =φ1X t-1 +φ2X t-2 +…+φ p X t-p +θ1∈ t-1 +…+θ q ∈ t-q +∈ t Among them, X t is the time series value, φ is the autoregressive parameter, θ is the moving average parameter, and ε is the white noise.

10. The mechatronic fault detection system according to claim 6, characterized in that: The user interface provides real-time monitoring data and fault warnings, and establishes fault alarm rules through threshold judgment and logistic regression models: y=β0+β1X1+β2X2+…+β n X n ; Where y is the predicted result value, usually 0 or 1, indicating a certain state. In fault detection, if y = 1, it means a fault has occurred; if y = 0, it means there is no fault; β0 is the intercept term, which represents the benchmark of the prediction result when the values ​​of all features X are 0. It is the initial value of the model when there is no influence of any input features. X1,X2,...,X n The input features represent the parameters or indicators monitored by the system. These features are used to predict the possibility of failure. β1,β2,...,β n are regression coefficients, corresponding to the input features X1, X2, ..., X n , these coefficients measure the influence of each feature on the prediction result; these coefficients measure the influence of each feature on the prediction result; The user interface uses virtual display and augmented reality modules to show operators the machine health status, fault location and recommended maintenance measures, enhancing the user experience; interactive alarm settings: users can select the sensitivity and type of alarms through touch screen or gestures.

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